Bioinformatics and Computational Tools for Next-Generation Sequencing Analysis in Clinical Genetics

Abstract

Clinical genetics has an important role in the healthcare system to provide a definitive diagnosis for many rare syndromes. It also can have an influence over genetics prevention, disease prognosis and assisting the selection of the best options of care/treatment for patients. Next-generation sequencing (NGS) has transformed clinical genetics making possible to analyze hundreds of genes at an unprecedented speed and at a lower price when comparing to conventional Sanger sequencing. Despite the growing literature concerning NGS in a clinical setting, this review aims to fill the gap that exists among (bio)informaticians, molecular geneticists and clinicians, by presenting a general overview of the NGS technology and workflow. First, we will review the current NGS platforms, focusing on the two main platforms Illumina and Ion Torrent, and discussing the major strong points and weaknesses intrinsic to each platform. Next, the NGS analytical bioinformatic pipelines are dissected, giving some emphasis to the algorithms commonly used to generate process data and to analyze sequence variants. Finally, the main challenges around NGS bioinformatics are placed in perspective for future developments. Even with the huge achievements made in NGS technology and bioinformatics, further improvements in bioinformatic algorithms are still required to deal with complex and genetically heterogeneous disorders.

Variants of uncertain significance in the era of high-throughput genome sequencing: a lesson from breast and ovary cancers

“…In addition to the remarkable benefits brought by the advent of NGS in cancer investigation, this technology has created a paradoxical relative shortage of answers in the face of the massive quantity of information generated by high-throughput technologies [11,12]. The identification of known somatic or germline mutations accelerates diagnosis, development, and use of drugs that specifically target tumor-driving mutations.…”

“…However, there are no available drugs for many known mutations or newly discovered variants, yet. While NGS is revealing deep tumor molecular characterization, it may not be sufficient to improve patient outcome or treatment, e.g., in the absence of actionable mutations [12]. Furthermore, NGS has accelerated the identification of many diverse diagnostic sub-groups of patients, but studying populations sufficiently large to be statistically significant is still challenging.…”

“…Another possible challenge in NGS use is the time it may take to receive sequencing results, which might be too long to match clinical needs [11]. Although NGS costs have dramatically decreased in absolute term, this technology is not always financially sustainable in all research and clinical environments [12]. Due to the great number of variants obtained, NGS data are complex and uncertainty may arise in physicians interpreting the results [11,13].…”

Applying next-generation sequencing to unravel the mutational landscape in viral quasispecies

“…The other variant callers include ShoRAH (Zagordi et al, 2011) and its extension (McElroy et al, 2013), SNVer (Wei et al, 2011), deepSNV (Gerstung et al, 2012), SAMtools (Li, 2011), GATK (McKenna et al, 2010, Ion-Torrent specific TVC and others. For comparison and evaluation of different methods, please refer to the dedicated reviews or comparative work (Hwang et al, 2015;Lee et al, 2020;Pereira et al, 2020). In short, each method has its own pros and cons.…”

“…Several bioinformatics tools or pipelines for variant calling have been developed specifically for studying viral variants (Huber et al, 2017;McElroy et al, 2013;Verbist et al, 2015;Zagordi et al, 2010) and calculating the complexity of a quasispecies as well as measuring the genetic distance between two similar quasispecies (Marinier et al, 2019). Many more variants callers have been discussed and compared in dedicated reviews or methodological comparison papers (Hwang et al, 2015;Lee et al, 2020;Pereira et al, 2020). Implementing an existing variant-calling tool in NGS data analysis is relatively simple and saves additional costs for sample preparation.…”